What makes a machine learning algorithm a low variance one or a high variance one?
What this is talking about is how much a machine learning algorithm is good at "memorizing" the data. Decision trees, for their nature, tend to overfit very easily, this is because they can separate the space along very non-linear curves, especially if you get a very deep tree. Simpler algorithms, on the other hand, tend to separate the space along linear hyper surfaces, and therefore tend to under-fit the data and may not give very good prediction, but may behave better on new unseen data which is very different from the training data.